Books

• Brazdil P., Giraud-Carrier C., Soares C., Vilalta R. (2008)
  Metalearning: Applications to Data Mining.
  Springer Verlag. ISBN: 978-3-540-73262-4

 Journal Papers and Book Chapters

• Ghosh S., Stepinski T., Vilalta R., (2009)
  Automatic Annotation of Planetary Surfaces with Geomorphic Labels.
  IEEE Transactions on Geoscience and Remote Sensing. Volume 47, Issue 11.
   
• Vilalta R., Stepinski T. (2008)
  Cluster Validation.
  Encyclopedia of Data Warehousing and Data Mining, 2nd edition, J. Wang Ed.
   
  
• Giraud-Carrier C., Brazdil P. and Soares C., Vilalta R. (2008)
  Meta-Learning.
  Encyclopedia of Data Warehousing and Data Mining, 2nd edition, J. Wang Ed.
  


• Stepinski T., Vilalta R., Achari M., (2007)
  Machine Learning Tools for Automatic Mapping of Martian Landforms.
  IIIE Intelligent Systems, 22(6).  
  


• Vilalta R., Stepinski T., Achari M., (2007)
  An Efficient Approach to External Cluster Assessment with an Application to Martian Topography.
  Data Mining and Knowledge Discovery Journal. Vol. 14, pp. 1-23. Springer Netherlands.  
  


• Eick C., Rouhana A., Bagherjeiran A., Vilalta R. (2006)
  Using Clustering to Learn Distance Functions for Supervised Similarity Assessment.
  Journal of Engineering Applications of Artificial Intelligence. Vol. 19, Issue 4, pp. 395-401.
  


• Stepinski T., Vilalta R. (2005)
  Digital Topography Models for Martian Surfaces.
  IEEE Geoscience and Remote Sensing Letters. Vol. 2 (3) pp. 260-264.  
  


• Vilalta R., Giraud-Carrier C., Brazdil P. (2005)
  Meta-Learning: Concepts and Techniques.
  Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers.
  Oded Maimon and Lior Rokach, Editors. Springer Publishers.
  


• Vilalta R., Giraud-Carrier C., Brazdil P. Soares C. (2004)
  Using Meta-Learning to Support Data Mining.
  International Journal of Computer Science and Applications. Vol. 1, No. 1, pp. 31-45.
  


• Giraud-Carrier C., Vilalta R., Brazdil P. (2004)
  Introduction to the Special Issue on Meta-Learning.
  Machine Learning, Vol. 54, No. 3 pp. 187-193.
  


• Vilalta R., Oblinger D. (2003)
  Evaluation Metrics in Classification: A Quantification of Distance-Bias.
  Computational Intelligence, Vol. 29, No. 3, pp. 264-283.  
  


• Vilalta R., Drissi Y. (2002).
  A Perspective View and Survey Of Meta-Learning.
  Journal of Artificial Intelligence Review, 18, No. 2, pp. 77-95.
  


• Vilalta R., Apte C., Hellerstein J., Ma S., Weiss S. (2002)
  Predictive Algorithms in the Management of Computer Systems.
  IBM Systems Journal, Special Issue on Artificial Intelligence, Vol 41, No. 3.
  

Conference and Workshop Papers

• Vilalta R., Valerio R., Ocegueda-Hernandez F., Watts G. (2009)
  The Effect of the Fragmentation Problem in Decision Tree Learning Applied to the Search for Single Top Quark Production.
  17th International Conference on Computing in High Energy and Nuclear Physics (CHEP-09), Prague, Czech Republic. Journal of Physics: Conference Series.  
  


• Jiamthapthaksin R., Eick C., Vilalta R. (2009)
  A Framework for Multi-objective Clustering and its Application to Co-location Mining.
  International Conference on Advanced Data Mining and Applications (ADMA-09), Beijing China.
  


• Sun, C., Vilalta R. (2007)
  Data Selection using SASH Trees for Support Vector Machines.
  5th International Conference on Machine Learning and Data Mining (MLDM-07), Leipzig, Germany.
  


• Ghosh S., Stepinski T., Vilalta R. (2007)
  Automatic Mapping of Martian Landforms Using Segmentation-Based Classification.
  38th Lunar and Planetary Science Conference. League City, Texas.  
  


• Stepinski T., Ghosh S., Vilalta R. (2007)
  Machine Learning for Automatic Mapping of Planetary Surfaces.
  Nineteenth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-07).  
  


• Subhlok J., Johnson O., Subramaniam V., Vilalta R., Yun C. (2007)
  Tablet PC Video based Hybrid Coursework in Computer Science: Report from a Pilot Project.
  38th ACM Technical Symposium on Computer Science Education (SIGCSE-2007).  
  


• Vilalta R., Mutchler G., Taylor S., Knuteson B. (2006)
  Automatic Signal Enhancement in Particle Physics using Multivariate Classification and Physical Constraints.
  Ninth Workshop on Mining Scientific and Engineering Datasets (MSD06), in conjunction with the Sixth SIAM International Conference on Data Mining, Bethesda, Maryland.  
  


• Stepinski T., Ghosh S., Vilalta R. (2006)
  Automatic Recognition of Landforms on Mars Using Terrain Segmentation and Classification.
  Ninth International Conference on Discovery Science (DS-2006), Barcelona, Spain.  
  


• Vilalta R. (2006)
  Identifying and Characterizing Class-Clusters to Explain Learning Performance.
  AAAI 2006 Spring Symposia: What Went Wrong and Why: Lessons from AI Research and Applications. Ed. Dan Shapiro. Stanford University, CA.  
  


• Vilalta R., Sarda P., Mutchler G., Padley P. (2005)
  Signal Enhancement Using Classification Techniques and Physical Constraints.
  Conference on Statistical Problems in Particle Physics, Astrophysics and Cosmology (PHYSTAT05), Oxford, U.K.
  


• Bagherjeiran A., Eick C., Chen C., Vilalta R. (2005)
  Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience.
  Fifth IEEE International Conference on Data Mining (ICDM05), New Orleans, Louisiana.
  


• Knuteson B., Vilalta R. (2005)
  Testing Theories in Particle Physics Using Maximum Likelihood and Adaptive Bin Allocation.
  9th European Conference on Principles and Practices of Knowledge Discovery in Databases (PKDD05), Porto, Portugal.
  


• Bagherjeiran A., Vilalta R. Eick C. (2005)
  Content-Based Image Retrieval Through a Multi-Agent Meta-Learning Framework.
  17th IEEE Conference on Tools with Artificial Intelligence (ICTAI05), Hong Kong.
  


• Eick C., Rouhana A., Bagherjeiran A., Vilalta R.(2005)
  Using Clustering to Learn Distance Functions for Supervised Similarity Assessment.
  International Conference on Machine Learning and Data Mining (MLDM05), Leipzig Germany.
  


• Vilalta R., Stepinski T., Achari M., Ocegueda-Hernandez F.(2004)
  A Quantification of Cluster Novelty with an Application to Martian Topography.
  8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD04).
  


• Eick C., Zeidat N., Vilalta R. (2004)
  Using Representative-Based Clustering for Nearest Neighbor Dataset Editing.
  Fourth IEEE International Conference on Data Mining (ICDM04), Brighton U.K.
  


• Vilalta R., Achari M., Eick C.(2004)
  Piece-Wise Model Fitting Using Local Data Patterns.
  Sixteenth European Conference on Artificial Intelligence (ECAI04). Valencia, Spain.
  


• Vilalta R., Stepinski T. (2004)
  Thematic Maps of Martian Topography Generated by a Clustering Algorithm.
  35th Lunar and Planetary Science Conference (LPSC04). League City, Texas.
  


• Vilalta R., Achari M., Eick C. (2003)
  Class Decomposition Via Clustering: A New Framework For Low-Variance Classifiers.
  Proceedings of the Third IEEE International Conference on Data Mining (ICDM03), Melbourne, Florida.
  


• Vilalta R., Rish I. (2003)
  A Decomposition Of Classes Via Clustering To Explain And Improve Naive Bayes.
  Proceedings of the European Conference on Machine Learning (ECML03). Cavtat-Dubrovnik, Croatia. (Best Paper Award).
  


• Vilalta R., Achari M. (2003)
  A Hierarchical Approach to Classification for Systems with Complex Low-Level Interactions.
  Proceedings of the IEEE International Symposium on Intelligent Control (ISIC03), Houston TX.
  


• Vilalta R., Padley P., Lee S.J., Sun C., Bagherjeiran A. (2003)
  Evaluating and Constructing Features For Identification of Tau Leptons.
  Proceedings of the Conference on Statistical Problems in Particle Physics, Astrophysics, and Cosmology (PHYSTAT03). Stanford CA.  
  


• Sahoo R. K., Oliner A. J., Rish I., Gupta M., Moreira J. E., Ma S., Vilalta R. (2003)
  Critical Event Prediction for Proactive Management in Large-scale Computer Clusters.
  The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD03), Washington DC.
  


• Stepinski T., Vilalta R., Achari M., McGovern P.J. (2003)
  Algorithmic Classification of Drainage Networks on Mars and its Relation to Martial Geological Units.
  34th Lunar and Planetary Science Conference (LPSC03). League City, Texas.
  


• Vilalta R., Stepinski T. (2003)
  Classification of Martian Terrain Using Automated Discovery of Structure Algorithm Applied to Digital Topography.
  Meeting of the American Geophysical Union (AGU03), San Francisco CA.
  


• Vilalta R., Ma Sheng (2002).
  Predicting Rare Events inTemporal Domains.
  Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 02), Maebashi Japan.
  


• Domeniconi C., Perng C., Vilalta R., Ma S. (2002)
  A Classification Approach for Prediction of Target Events in Temporal Sequences.
  Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'02), Helsinki, Finland.
  


• Vilalta R., Drissi Y. (2002)
  A Characterization of Difficult Problems in Classification.
  Proceedings of the International Conference on Machine Learning and Applications (ICMLA02), Las Vegas, Nevada.
  


• Sahoo R., Bae M., Vilalta R., Moreira J., Ma S., Gupta M. (2002)
  Providing Persistent and Consistent Resources Through Event Log Analysis and Prediction for Large-Scale Computing Systems.
  Workshop on Self-Healing, Adaptive, and Self-Managed Systems as part of the 16th Annual ACM International Conference on Supercomputing.  
  


• Vilalta R., Brodie M., Oblinger D., Rish I. (2001)
  A Unified Framework For Evaluation Metrics In Classification Using Decision Trees.
  Proceedings of the 12th European Conference on Machine Learning (ECML01), Freiburg, Germany. 
  


• Vilalta R., Ma S., Hellerstein J. (2001)
  Rule Induction of Computer Events
  Proceedings of  the 12th IFIP/IEEE International Workshop on Distributed Systems: Operations & Management (DSOM01), Nancy, France. 
  


• Vilalta R., Drissi Y. (2001)
  Research Directions in Meta-Learning
  Proceedings of the International Conference on Artificial Intelligence, (ICAI01) Las Vegas, Nevada. Ed. H. R. Arabnia. 
  


• Vilalta R., Oblinger D. (2000)
  A Quantification Of Distance-Bias Between Evaluation Metrics In Classification
  Proceedings of the 17th International Conference on Machine Learning (ICML00). Stanford University, Stanford, CA., pp.1087-1094. 
  


• Vilalta R., Rish I., Oblinger D. (2000)
  What Works Well Where in Inductive Learning?
  Workshop during the 17th International Conference on Machine Learning (ICML00). Stanford University, Stanford, CA., pp.1087-1094. 
  


• Vilalta R., Apte C., Weiss S. (2000)
  Operational Data Analysis: Improved Predictions Using Multi-Computer Pattern Detection.
  Proceedings of the International Workshop on Distributed Systems: Operations & Management (DSOM00), Austin Texas.
  


• Vilalta R. (1999)
  Understanding Accuracy Performance Through Concept Characterization And Algorithm Analysis.
  Workshop on Recent Advances in Meta-Learning and Future Work, as part of the 16th International Conference on Machine Learning, Bled Slovenia. Edited by Christophe Giraud-Carrier and Bernhard Pfahringer. pp 3-9.
  


• Vilalta R., Rendell L. (1997)
  Integrating Feature Construction with Multiple Classifiers in Decision Tree Induction
  Proceedings of the 14th International Conference on Machine Learning (ICML97). Vanderbilt University, Nashville, TN. Morgan Kaufman Publishers, San Francisco CA. pp 394-402. 
  


• Vilalta R., Blix G., Rendell L. (1997)
  Global Data Analysis and the Fragmentation problem in Decision Tree Induction
  Proceedings of the 9th European Conference on Machine Learning (ECML97). Lecture Notes in Artificial Intelligence. Vol. XXX. Springer-Verlag, Heinderberg, pp 312-326.   
  


• Perez E., Vilalta R., Rendell L. (1996)
  On the Importance of Change of Representation in Induction (Invited talk).
  Presented at the Workshop of Inductive Learning for the 1996 Canadian Conference on Artificial Intelligence.

Technical Reports

• Vilalta R., Blix G., Rendell L. (1995)
  The Value of Lookahead Feature Construction in Decision Tree Induction.
  Beckman Institute, University of Illinois at  Urbana-Champaign, Technical Report UIUC-BI-AI-95-01.

Thesis

• Vilalta R. (1998)
  On the Development of Inductive Learning Algorithms: Generating Flexible and Adaptable Concept Representations.
  University of Illinois at Urbana-Champaign. PhD Thesis.

Patents Issued

• Vilalta R., Irina R. (2005)
  Method and Apparatus for Selecting a Data Classification Model Using Meta-Learning.
  IBM T.J. Watson Research Center, YOR9-2000-0368US1. U.S. Patent 06842751.
  


• Vilalta R., Drissi Y. (2004)
  Method and Apparatus for Building a Data Classification Model Using Interactive Adaptive Learning  Algorithms.
  IBM T.J. Watson Research Center. , YOR9-2000-0507US1. U.S. Patent 06728689.